Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection
Cancer is still one of the most life threatening disease and by far it is still difficult to prevent, prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis can effectively increase the survival rate of patients. But early stage cancer is diffi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9020066/ |
| _version_ | 1818619813354799104 |
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| author | Qingguo Zhou Binbin Yong Qingquan Lv Jun Shen Xin Wang |
| author_facet | Qingguo Zhou Binbin Yong Qingquan Lv Jun Shen Xin Wang |
| author_sort | Qingguo Zhou |
| collection | DOAJ |
| description | Cancer is still one of the most life threatening disease and by far it is still difficult to prevent, prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed. In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization algorithm are also used to select optimized features from mass spectrometry data. The learned features are further evaluated on three cancer datasets. The experimental results demonstrate that the cancer detection accuracy by learned features is as high as 100%. As our main contribution, the deep autoencoder method used in this study is a feasible and powerful instrument for mass spectrometry feature learning and also cancer diagnosis. |
| first_indexed | 2024-12-16T17:43:27Z |
| format | Article |
| id | doaj.art-cb674a97fb514b9a84edd19f9833d6f1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:43:27Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cb674a97fb514b9a84edd19f9833d6f12022-12-21T22:22:32ZengIEEEIEEE Access2169-35362020-01-018451564516610.1109/ACCESS.2020.29776809020066Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer DetectionQingguo Zhou0https://orcid.org/0000-0001-8054-5446Binbin Yong1https://orcid.org/0000-0002-6460-8950Qingquan Lv2Jun Shen3https://orcid.org/0000-0002-9403-7140Xin Wang4School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Computing and Information Technology, University of Wollongong, Wollongong, NSW, AustraliaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaCancer is still one of the most life threatening disease and by far it is still difficult to prevent, prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed. In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization algorithm are also used to select optimized features from mass spectrometry data. The learned features are further evaluated on three cancer datasets. The experimental results demonstrate that the cancer detection accuracy by learned features is as high as 100%. As our main contribution, the deep autoencoder method used in this study is a feasible and powerful instrument for mass spectrometry feature learning and also cancer diagnosis.https://ieeexplore.ieee.org/document/9020066/Early cancer diagnosisdeep autoencoderparticle swarm optimizationmass spectrometry feature learning |
| spellingShingle | Qingguo Zhou Binbin Yong Qingquan Lv Jun Shen Xin Wang Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection IEEE Access Early cancer diagnosis deep autoencoder particle swarm optimization mass spectrometry feature learning |
| title | Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection |
| title_full | Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection |
| title_fullStr | Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection |
| title_full_unstemmed | Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection |
| title_short | Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection |
| title_sort | deep autoencoder for mass spectrometry feature learning and cancer detection |
| topic | Early cancer diagnosis deep autoencoder particle swarm optimization mass spectrometry feature learning |
| url | https://ieeexplore.ieee.org/document/9020066/ |
| work_keys_str_mv | AT qingguozhou deepautoencoderformassspectrometryfeaturelearningandcancerdetection AT binbinyong deepautoencoderformassspectrometryfeaturelearningandcancerdetection AT qingquanlv deepautoencoderformassspectrometryfeaturelearningandcancerdetection AT junshen deepautoencoderformassspectrometryfeaturelearningandcancerdetection AT xinwang deepautoencoderformassspectrometryfeaturelearningandcancerdetection |